Hirschsprung’s disease is a motility disorder that requires the assessment of the Auerbach’s (myenteric) plexus located in muscularis propria layer. In this paper, we describe a fully automated method for segmenting muscularis propria (MP) from histopathology images of intestinal specimens using a method based on convolutional neural network (CNN). Such a network has the potential to learn intensity, textural, and shape features from the manual segmented images to accomplish distinction between MP and non-MP tissues from histopathology images. We used a dataset consisted of 15 images and trained our model using approximately 3,400,000 image patches extracted from six images. The trained CNN was employed to determine the boundary of MP on 9 test images (including 75,000,000 image patches). The resultant segmentation maps were compared with the manual segmentations to investigate the performance of our proposed method for MP delineation. Our technique yielded an average Dice similarity coefficient (DSC) and absolute surface difference (ASD) of 92.36 ± 2.91% and 1.78 ± 1.57 mm2 respectively, demonstrating that the proposed CNNbased method is capable of accurately segmenting MP tissue from histopathology images.
Myocardial scar geometry may influence the sensitivity of predicting risk for ventricular tachycardia (VT) using computational models of the heart. This study aims to compare the differences in reconstructed geometry of scar generated using two-dimensional (2D) versus three-dimensional (3D) late gadolinium-enhanced magnetic resonance (LGE-MR) images. We used a retrospectively-acquired dataset of 17 patients with myocardial scar who underwent both 2D and 3D LGE-MR imaging. We segmented the scar manually in both 2D and 3D LGE-MRI using a multi-planar image processing software. We then reconstructed the 2D scar segmentation boundaries from 2D LGE-MRI to 3D surfaces using a LogOdds-based interpolation method. Finally, we assessed the 3D models of scar in both 3D and 2D-reconstructed techniques using several shape and volume metrics such as, fractal dimensions, number of connected components, mean scar volume, and normalized scar volume. The higher fractal dimension resulted for 3D may indicate that the 3D LGE-MRI produces a more complex surface geometry by better capturing the intact geometry of the scar. The 2D LGE-MRI produced a larger normalized scar volume (19.48±10 cm3) than the 3D LGE-MRI (10.92±7.12 cm3). We also provided a statistical analysis on the scar volume differences acquired from 2D and 3D LGE-MRI.